Perceiving light versus material

Abstract

Humans rarely confuse variations in light intensity, such as shadows, shading, light sources and specular reflections, from variations in material properties, such as albedo or pigment. This review explores the cues, or regularities in the visual world that evidence suggests vision exploits to discriminate light from material. These cues include luminance relations, figural relations, 3D-shape, depth, colour, texture, and motion. On the basis of an examination of the cues together with the behavioural evidence that they are used by vision, I propose a set of heuristics that may guide vision in the task of distinguishing between light and material. I argue that while there is evidence for the use of these heuristics, little is known about their relative importance and the manner in which they are combined in naturalistic situations where there are multiple cues as to what is light and what is material. Finally, I discuss two theoretical frameworks, the generic view principle and Bayesian estimation, that are beginning to help us understand the visual processes involved in distinguishing between light and material.

Lightness constancy and illumination discounting

“…Although the importance of edge classification for lightness perception was realised a few decades ago (Gilchrist et al 1983), it has not been paid as much attention as it merits, perhaps because it has seemed that it would be possible to develop a theory of anchoring processes without resorting to this process (Gilchrist, 2006). Nevertheless, in the broader context of colour vision, a few illumination cues have been established (for a comprehensive review, see Kingdom, 2008). For instance, the straightness and fuzziness of a luminance edge were found to testify in favour of its being produced by an illumination edge; curvature, closedness, and sharpness, of its being produced by a material edge (Logvinenko, Adelson, Ross, & Somers, 2005).…”

“…It is vitally important from the ecological point of view to distinguish between these (e.g., Gibson, 1979). Human observers have been found to be rather good at distinguishing between changes produced by illumination and reflectance in visual scenes (e.g., Craven & Foster, 1992;Foster et al, 2001;Foster, 2003;Kingdom, 2008).…”

Correlation-Based Intrinsic Image Extraction from a Single Image

“…Finlayson et al's colour based method [9] defines an illumination-invariant colour space to discriminate shadows from reflectance variations, but this requires a calibrated camera. Further, since humans can distinguish shadows from reflectance changes in monochrome images [10] colour cannot be the only cue that enables such a separation.…”

What makes cast shadows hard to see?

“…However, why should object recognition be at the core of shadow-related slowing? Here, the concept of combined heuristics for distinguishing "lighting" from "material" (Kingdom, 2008) may provide a framework within which to understand these effects. One might speculate that, in the upright shadow condition, such heuristics lead us to identify only the shadow casters (i.e., vertical bars) as "material" (i.e., objects), while the shadows (i.e., oblique bars) would be classified as lighting, not contributing to object recognition.…”